论文标题
对发展数据流的机器学习方法的标准化评估
Standardized Evaluation of Machine Learning Methods for Evolving Data Streams
论文作者
论文摘要
由于数据流的未指定和动态性质，在线机器学习需要强大而灵活的解决方案。但是，很难在现实条件下评估在线机器学习方法。因此，现有的工作通常借鉴不同的启发式方法和模拟，这些启发式和模拟不一定会产生有意义和可靠的结果。确实，在没有共同评估标准的情况下，通常不清楚在线学习方法在实践中或与类似工作相比将如何执行。在本文中，我们建议在不断发展的数据流中为高质量的机器学习提供一套全面的属性。特别是，我们讨论了在线预测建模，在线功能选择和概念漂移检测的合理绩效指标和评估策略。作为第一批作品之一，我们还研究了在线学习方法的解释性。拟议的评估标准是在称为Float的新的Python框架中提供的。 Float是完全模块化的，可以同时整合具有自定义代码的常见库，例如Scikit-Multiflow或River。 Float是开源的，可以在https://github.com/haugjo/float上访问。从这个意义上讲，我们希望我们的工作将有助于更标准化，可靠和现实的测试以及在线机器学习方法的比较。
Due to the unspecified and dynamic nature of data streams, online machine learning requires powerful and flexible solutions. However, evaluating online machine learning methods under realistic conditions is difficult. Existing work therefore often draws on different heuristics and simulations that do not necessarily produce meaningful and reliable results. Indeed, in the absence of common evaluation standards, it often remains unclear how online learning methods will perform in practice or in comparison to similar work. In this paper, we propose a comprehensive set of properties for high-quality machine learning in evolving data streams. In particular, we discuss sensible performance measures and evaluation strategies for online predictive modelling, online feature selection and concept drift detection. As one of the first works, we also look at the interpretability of online learning methods. The proposed evaluation standards are provided in a new Python framework called float. Float is completely modular and allows the simultaneous integration of common libraries, such as scikit-multiflow or river, with custom code. Float is open-sourced and can be accessed at https://github.com/haugjo/float. In this sense, we hope that our work will contribute to more standardized, reliable and realistic testing and comparison of online machine learning methods.
